Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.5 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_unique_basket_size is highly overall correlated with avg_ticket and 1 other fieldsHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
gross_revenue is highly overall correlated with avg_basket_size and 3 other fieldsHigh correlation
qty_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qty_items is highly overall correlated with avg_basket_size and 3 other fieldsHigh correlation
qty_products is highly overall correlated with avg_unique_basket_size and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qty_invoicesHigh correlation
avg_ticket is highly skewed (γ1 = 53.44421547)Skewed
frequency is highly skewed (γ1 = 24.88052885)Skewed
qty_returns is highly skewed (γ1 = 52.70290171)Skewed
avg_basket_size is highly skewed (γ1 = 44.67431359)Skewed
customer_id has unique valuesUnique
recency_days has 34 (1.1%) zerosZeros
qty_returns has 1481 (49.9%) zerosZeros

Reproduction

Analysis started2024-08-09 11:45:21.329505
Analysis finished2024-08-09 11:45:48.876381
Duration27.55 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

UNIQUE 

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:49.337583image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2024-08-09T08:45:49.572134image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17850 1
 
< 0.1%
17588 1
 
< 0.1%
14905 1
 
< 0.1%
16103 1
 
< 0.1%
14626 1
 
< 0.1%
14868 1
 
< 0.1%
18246 1
 
< 0.1%
17115 1
 
< 0.1%
16611 1
 
< 0.1%
15912 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

HIGH CORRELATION 

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2747.1004
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:49.797853image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1084.1
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10560.058
Coefficient of variation (CV)3.8440742
Kurtosis355.5067
Mean2747.1004
Median Absolute Deviation (MAD)672.05
Skewness16.802797
Sum8156141.2
Variance1.1151482 × 108
MonotonicityNot monotonic
2024-08-09T08:45:50.050683image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.96 2
 
0.1%
533.33 2
 
0.1%
889.93 2
 
0.1%
2053.02 2
 
0.1%
745.06 2
 
0.1%
379.65 2
 
0.1%
2092.32 2
 
0.1%
731.9 2
 
0.1%
1353.74 2
 
0.1%
331 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
136263.72 1
< 0.1%
124564.53 1
< 0.1%
116725.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.288649
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:50.301390image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756171
Coefficient of variation (CV)1.2094852
Kurtosis2.7780386
Mean64.288649
Median Absolute Deviation (MAD)26
Skewness1.7983969
Sum190873
Variance6046.0221
MonotonicityNot monotonic
2024-08-09T08:45:50.543493image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
2 85
 
2.9%
3 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
22 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qty_invoices
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7217918
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:50.889730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.847316
Coefficient of variation (CV)1.5462492
Kurtosis190.04523
Mean5.7217918
Median Absolute Deviation (MAD)2
Skewness10.743151
Sum16988
Variance78.275
MonotonicityNot monotonic
2024-08-09T08:45:51.252530image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 786
26.5%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 70
 
2.4%
11 54
 
1.8%
Other values (47) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 786
26.5%
3 498
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 70
 
2.4%
10 54
 
1.8%
ValueCountFrequency (%)
206 1
< 0.1%
198 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qty_items
Real number (ℝ)

HIGH CORRELATION 

Distinct1670
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1606.4187
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:51.479365image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.4
Q1296
median639
Q31399
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation5882.5587
Coefficient of variation (CV)3.6619088
Kurtosis467.23973
Mean1606.4187
Median Absolute Deviation (MAD)420
Skewness17.879512
Sum4769457
Variance34604497
MonotonicityNot monotonic
2024-08-09T08:45:51.737344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 9
 
0.3%
88 9
 
0.3%
246 8
 
0.3%
260 8
 
0.3%
288 8
 
0.3%
272 8
 
0.3%
84 8
 
0.3%
134 8
 
0.3%
394 7
 
0.2%
Other values (1660) 2885
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
79879 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
62812 1
< 0.1%
58243 1
< 0.1%
57772 1
< 0.1%

qty_products
Real number (ℝ)

HIGH CORRELATION 

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.66319
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:51.986786image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.2448
Coefficient of variation (CV)2.1949927
Kurtosis354.42387
Mean122.66319
Median Absolute Deviation (MAD)44
Skewness15.678323
Sum364187
Variance72492.76
MonotonicityNot monotonic
2024-08-09T08:45:52.236583image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 46
 
1.5%
20 38
 
1.3%
35 35
 
1.2%
15 33
 
1.1%
29 32
 
1.1%
19 32
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
18 30
 
1.0%
27 30
 
1.0%
Other values (459) 2630
88.6%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 27
0.9%
10 27
0.9%
ValueCountFrequency (%)
7837 1
< 0.1%
5586 1
< 0.1%
5095 1
< 0.1%
4577 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1636 1
< 0.1%

avg_ticket
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.893306
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:52.563966image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.940811
Q324.97963
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.860296

Descriptive statistics

Standard deviation1036.9345
Coefficient of variation (CV)19.982048
Kurtosis2890.7065
Mean51.893306
Median Absolute Deviation (MAD)5.9641157
Skewness53.444215
Sum154071.22
Variance1075233.2
MonotonicityNot monotonic
2024-08-09T08:45:53.045041image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2
 
0.1%
4.162 2
 
0.1%
14.47833333 2
 
0.1%
18.15222222 1
 
< 0.1%
13.92736842 1
 
< 0.1%
36.24411765 1
 
< 0.1%
29.78416667 1
 
< 0.1%
22.8792623 1
 
< 0.1%
20.51104167 1
 
< 0.1%
149.025 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

HIGH CORRELATION 

Distinct290
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.092287
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:53.347649image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125
median48
Q385
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)60

Descriptive statistics

Standard deviation63.614645
Coefficient of variation (CV)0.94816629
Kurtosis4.8867434
Mean67.092287
Median Absolute Deviation (MAD)26
Skewness2.0639464
Sum199197
Variance4046.823
MonotonicityNot monotonic
2024-08-09T08:45:53.611072image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 49
 
1.7%
14 45
 
1.5%
46 43
 
1.4%
21 43
 
1.4%
22 43
 
1.4%
38 42
 
1.4%
17 41
 
1.4%
27 41
 
1.4%
28 41
 
1.4%
26 41
 
1.4%
Other values (280) 2540
85.6%
ValueCountFrequency (%)
1 17
0.6%
2 15
0.5%
3 19
0.6%
4 27
0.9%
5 18
0.6%
6 21
0.7%
7 31
1.0%
8 24
0.8%
9 27
0.9%
10 27
0.9%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1224
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11378831
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:53.855695image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049418605
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.033078735

Descriptive statistics

Standard deviation0.40815636
Coefficient of variation (CV)3.5869797
Kurtosis989.36618
Mean0.11378831
Median Absolute Deviation (MAD)0.012191338
Skewness24.880529
Sum337.8375
Variance0.16659162
MonotonicityNot monotonic
2024-08-09T08:45:54.103398image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 17
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.08333333333 15
 
0.5%
0.09090909091 15
 
0.5%
0.03448275862 14
 
0.5%
0.02941176471 14
 
0.5%
0.01923076923 13
 
0.4%
0.02564102564 13
 
0.4%
Other values (1214) 2637
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5308310992 1
 
< 0.1%
0.5 3
 
0.1%

qty_returns
Real number (ℝ)

SKEWED  ZEROS 

Distinct173
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.255978
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:54.353594image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile63
Maximum80995
Range80995
Interquartile range (IQR)6

Descriptive statistics

Standard deviation1503.4837
Coefficient of variation (CV)28.771515
Kurtosis2833.6407
Mean52.255978
Median Absolute Deviation (MAD)1
Skewness52.702902
Sum155148
Variance2260463.1
MonotonicityNot monotonic
2024-08-09T08:45:54.623640image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 295
 
9.9%
3 169
 
5.7%
6 93
 
3.1%
2 87
 
2.9%
4 71
 
2.4%
5 43
 
1.4%
12 43
 
1.4%
8 40
 
1.3%
7 38
 
1.3%
Other values (163) 609
20.5%
ValueCountFrequency (%)
0 1481
49.9%
1 295
 
9.9%
2 87
 
2.9%
3 169
 
5.7%
4 71
 
2.4%
5 43
 
1.4%
6 93
 
3.1%
7 38
 
1.3%
8 40
 
1.3%
9 36
 
1.2%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
4824 1
< 0.1%
4027 1
< 0.1%
2302 2
0.1%
1776 1
< 0.1%
1608 1
< 0.1%
1589 1
< 0.1%
1515 1
< 0.1%
1278 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

HIGH CORRELATION 

Distinct1009
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.15117
Minimum1
Maximum299.70588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:54.887514image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.3454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.70588
Range298.70588
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.512963
Coefficient of variation (CV)0.88089984
Kurtosis27.697928
Mean22.15117
Median Absolute Deviation (MAD)8.2
Skewness3.4988031
Sum65766.825
Variance380.75571
MonotonicityNot monotonic
2024-08-09T08:45:55.128883image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 54
 
1.8%
14 40
 
1.3%
11 38
 
1.3%
9 33
 
1.1%
18 33
 
1.1%
1 32
 
1.1%
20 31
 
1.0%
10 30
 
1.0%
16 29
 
1.0%
17 28
 
0.9%
Other values (999) 2621
88.3%
ValueCountFrequency (%)
1 32
1.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.333333333 2
 
0.1%
1.5 8
 
0.3%
1.568181818 1
 
< 0.1%
1.571428571 1
 
< 0.1%
1.666666667 4
 
0.1%
1.833333333 1
 
< 0.1%
2 24
0.8%
ValueCountFrequency (%)
299.7058824 1
< 0.1%
259 1
< 0.1%
203.5 1
< 0.1%
148 1
< 0.1%
145 1
< 0.1%
136.125 1
< 0.1%
135.5 1
< 0.1%
127 1
< 0.1%
122 1
< 0.1%
118 1
< 0.1%

avg_basket_size
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1974
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.39271
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2024-08-09T08:45:55.380218image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172
Q3281.5
95-th percentile599.52
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation791.55574
Coefficient of variation (CV)3.173933
Kurtosis2255.6274
Mean249.39271
Median Absolute Deviation (MAD)82.75
Skewness44.674314
Sum740446.95
Variance626560.48
MonotonicityNot monotonic
2024-08-09T08:45:55.676516image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
82 9
 
0.3%
86 9
 
0.3%
60 8
 
0.3%
140 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
163 8
 
0.3%
Other values (1964) 2881
97.0%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

Interactions

2024-08-09T08:45:45.652413image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:21.895516image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:24.072879image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:26.121807image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:28.394749image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:30.374139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:32.726016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:34.898277image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:36.936734image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:39.035652image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:41.396651image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:43.589039image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:45.825120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:22.120986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:24.237204image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:26.444369image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:28.550124image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:30.550861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:32.894489image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:35.054554image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:37.105388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:39.204826image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:41.565278image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:43.757998image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:45.998753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:22.281381image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:24.398471image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:26.606234image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:28.707299image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:30.721813image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:33.065060image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:35.216802image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:37.271564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:39.369848image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:41.733828image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:43.926498image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:46.183891image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:22.473492image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:24.571959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:26.784827image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:28.870015image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:30.906925image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:33.244653image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:35.386236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:37.453463image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:39.784794image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:41.912282image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:44.097672image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:46.368543image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:22.671764image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:24.723336image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:26.944502image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:29.012829image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:31.067440image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:33.404427image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:35.542056image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:37.611362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:39.958045image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:42.074742image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:44.252754image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:46.694350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:22.850993image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:24.898935image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:27.136769image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:29.183100image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:31.250241image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:33.592924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:35.726762image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:37.800201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:40.141029image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:42.267143image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:44.436751image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:47.039351image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:23.029436image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:25.077634image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:27.322477image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:29.359201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:31.454767image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:33.774954image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:35.896919image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:37.981183image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:40.328282image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:42.469256image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:44.616208image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:47.345536image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:23.195112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:25.239058image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:27.488038image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:29.512821image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:31.625441image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:33.945488image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:36.051764image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:38.140744image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:40.491536image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:42.643279image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:44.778681image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:47.566179image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:23.362800image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:25.414646image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:27.662239image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:29.688318image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:31.806561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:34.132396image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:36.218390image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:38.317837image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:40.673347image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:42.831350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:44.948153image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:47.749569image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:23.533994image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:25.591385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:27.840415image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:29.870070image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:31.989221image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:34.320340image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:36.387939image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:38.496027image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:40.851112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:43.013470image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:45.118505image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:47.948593image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:23.712661image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:25.770601image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:28.025008image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:30.041658image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:32.174760image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:34.512038image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:36.587982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:38.681982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:41.041290image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:43.197983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:45.300729image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:48.127660image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:23.889531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:25.940790image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:28.199602image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:30.202079image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:32.537366image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:34.712915image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:36.763645image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:38.850142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:41.215147image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:43.398622image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-08-09T08:45:45.468267image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-08-09T08:45:55.879047image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
avg_basket_sizeavg_recency_daysavg_ticketavg_unique_basket_sizecustomer_idfrequencygross_revenueqty_invoicesqty_itemsqty_productsqty_returnsrecency_days
avg_basket_size1.000-0.0770.1890.448-0.1230.0270.5760.1010.7290.3840.210-0.098
avg_recency_days-0.0771.000-0.1220.0480.019-0.880-0.250-0.262-0.230-0.168-0.3920.109
avg_ticket0.189-0.1221.000-0.611-0.1300.0900.2460.0610.168-0.3770.1980.047
avg_unique_basket_size0.4480.048-0.6111.000-0.007-0.0720.2910.0260.3210.6990.006-0.107
customer_id-0.1230.019-0.130-0.0071.000-0.002-0.0760.025-0.0700.013-0.0640.001
frequency0.027-0.8800.090-0.072-0.0021.0000.0900.0780.0800.0350.2340.018
gross_revenue0.576-0.2500.2460.291-0.0760.0901.0000.7710.9270.7440.359-0.415
qty_invoices0.101-0.2620.0610.0260.0250.0780.7711.0000.7170.6900.283-0.502
qty_items0.729-0.2300.1680.321-0.0700.0800.9270.7171.0000.7310.335-0.408
qty_products0.384-0.168-0.3770.6990.0130.0350.7440.6900.7311.0000.226-0.435
qty_returns0.210-0.3920.1980.006-0.0640.2340.3590.2830.3350.2261.000-0.115
recency_days-0.0980.1090.047-0.1070.0010.018-0.415-0.502-0.408-0.435-0.1151.000

Missing values

2024-08-09T08:45:48.423013image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-09T08:45:48.738606image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqty_invoicesqty_itemsqty_productsavg_ticketavg_recency_daysfrequencyqty_returnsavg_unique_basket_sizeavg_basket_size
017850.05391.21372.034.01733.0297.018.15222235.017.00000021.08.73529450.970588
113047.03232.5956.09.01390.0171.018.90403527.00.0283026.019.000000154.444444
212583.06705.382.015.05028.0232.028.90250023.00.04032350.015.466667335.200000
313748.0948.2595.05.0439.028.033.86607192.00.0179210.05.60000087.800000
415100.0876.00333.03.080.03.0292.0000008.00.07317122.01.00000026.666667
515291.04623.3025.014.02102.0102.045.32647123.00.04011527.07.285714150.142857
614688.05630.877.021.03621.0327.017.21978618.00.057221281.015.571429172.428571
717809.05411.9116.012.02057.061.088.71983635.00.03352041.05.083333171.416667
815311.060767.900.091.038194.02379.025.5434644.00.243316231.026.142857419.714286
916098.02005.6387.07.0613.067.029.93477647.00.0243900.09.57142987.571429
customer_idgross_revenuerecency_daysqty_invoicesqty_itemsqty_productsavg_ticketavg_recency_daysfrequencyqty_returnsavg_unique_basket_sizeavg_basket_size
562717727.01060.2515.01.0645.066.016.0643946.01.0000006.066.0645.000000
563717232.0421.522.02.0203.036.011.70888912.00.1538460.018.0101.500000
563817468.0137.0010.02.0116.05.027.4000004.00.4000000.02.558.000000
564913596.0697.045.02.0406.0166.04.1990367.00.2500000.083.0203.000000
565514893.01237.859.02.0799.073.016.9568492.00.6666670.036.5399.500000
565912479.0473.2011.01.0382.030.015.7733334.01.00000034.030.0382.000000
568014126.0706.137.03.0508.015.047.0753333.00.75000050.05.0169.333333
568613521.01092.391.03.0733.0435.02.5112414.00.3000000.0145.0244.333333
569615060.0301.848.04.0262.0120.02.5153331.02.0000000.030.065.500000
571512558.0269.967.01.0196.011.024.5418186.01.000000102.011.0196.000000